MDLIVE, Inc. (20240331136). MACHINE LEARNING TO PREDICT MEDICAL IMAGE VALIDITY AND TO PREDICT A MEDICAL DIAGNOSIS simplified abstract
Contents
MACHINE LEARNING TO PREDICT MEDICAL IMAGE VALIDITY AND TO PREDICT A MEDICAL DIAGNOSIS
Organization Name
Inventor(s)
Nakort E. Valles Leon of Weston FL (US)
MACHINE LEARNING TO PREDICT MEDICAL IMAGE VALIDITY AND TO PREDICT A MEDICAL DIAGNOSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240331136 titled 'MACHINE LEARNING TO PREDICT MEDICAL IMAGE VALIDITY AND TO PREDICT A MEDICAL DIAGNOSIS
Simplified Explanation: The patent application describes a method for providing telehealth services, where a system receives inputs from a patient device including health data and an image of an ailment. The system uses prediction models to determine the quality of the image and generate predictions of the ailment. These predictions are then transmitted to a healthcare provider device, establishing communication between the patient and provider. The system further receives feedback from the provider device, trains the prediction models, and continues the telehealth process.
Key Features and Innovation:
- Method for providing telehealth services using patient inputs and prediction models.
- Determination of image quality and generation of ailment predictions.
- Transmission of predictions and images to healthcare provider device.
- Establishment of communication between patient and provider devices.
- Feedback loop for provider confirmation or rejection of predictions and images.
- Training of prediction models for continuous improvement.
Potential Applications: The technology can be applied in telemedicine, remote healthcare consultations, and diagnostic services.
Problems Solved: The technology addresses the need for efficient telehealth services, accurate ailment predictions, and seamless communication between patients and healthcare providers.
Benefits:
- Improved access to healthcare services.
- Faster and more accurate diagnosis of ailments.
- Enhanced communication between patients and healthcare providers.
- Continuous training of prediction models for better performance.
Commercial Applications: Title: Telehealth Services Optimization for Remote Diagnostics This technology can be utilized by telehealth companies, healthcare providers, and medical institutions to offer remote diagnostic services, improve patient outcomes, and streamline healthcare delivery processes.
Prior Art: There may be prior art related to telehealth services, image prediction models, and ailment prediction machine learning models in the field of healthcare technology and telemedicine.
Frequently Updated Research: Researchers may be conducting studies on the effectiveness of telehealth services, the accuracy of prediction models in diagnosing ailments remotely, and the impact of continuous training on machine learning models in healthcare.
Questions about Telehealth Services Optimization for Remote Diagnostics: 1. How does the technology ensure the privacy and security of patient data during telehealth consultations? 2. What are the potential challenges in implementing this technology in healthcare systems?
Original Abstract Submitted
a method performed by a system for providing telehealth services. the method includes receiving inputs from a patient device. the inputs include health data and an image of an ailment. with an image prediction model, the step of determining if the image is of sufficient quality to generate predictions of the ailment. with an ailment prediction model, the method generates one or more predictions of the ailment based on the health data and the image. the method continues with transmitting the predictions and the image to a healthcare provider device. the method proceeds with establishing communication between the patient and healthcare provider devices. the method continues with receiving inputs from the healthcare provider device. the inputs include a confirmation or a rejection of the image and a confirmation or a rejection the predictions. the method proceeds with training the image prediction model and the ailment prediction machine learning model.
